As tools like generative AI become increasingly mainstream, the quality and accessibility of enterprise data has become more important than ever before. Many organizations are rethinking their data governance programs as a result. But according to Kevin Lewis, a leading data analytics expert at AWS, 90% of organizations make the same mistake when they start a data governance program.
“Most organizations have a generic goal to make data easier for people to find or to improve data quality generally,” Lewis says. “But it’s a big mistake to focus on data governance in isolation rather than starting your data governance journey by identifying business initiatives that will prove transformational for the company.”
Lewis sat down with ZDNET Editor in Chief Jason Hiner to talk about how to build a successful data governance program, the role of data governance in technology innovation, and how emerging technologies like generative AI are changing the way we work.
(The following interview has been edited for length and clarity.)
JH: Kevin, let’s start with what you do at Amazon.
KL: As a part of AWS Professional Services, I work with customers to help them achieve their data analytics program goals. I get involved in data strategy, business alignment, data governance, and organizational structure.
JH: How would you define data governance?
KL: Data governance helps organizations accelerate innovation with data and data-driven decisions by making it easy for the right people and applications to securely and safely find, access, and share the right data when they need it. I like to think of it as simply making sure your data is in a condition that can meet the needs of your targeted business initiatives.
A key element in data governance success is finding the balance between access and control that enables innovation – and the balancing point is different for each organization. When organizations exercise too much control, the data gets locked up in silos and users are not able to access the data when they need it. This not only stifles creativity, but also leads to the creation of shadow IT systems that leave data out of date, and unsecured. On the other hand, when organizations provide too much access, data ends up in applications and data stores that increase the risk of data proliferation and leakage.